Adaptive Neural Control for Safe Human-Robot Interaction
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This thesis studies safe human-robot interaction utilizing the neural adaptive control design. First, novel tangent and secant barrier Lyapunov functions are constructed to provide stable position and velocity constrained controls, respectively. Then, neural backpropagation and the concept of the inverse differential Riccati equation are utilized to achieve the impedance adaption control for assistive human-robot interaction, and the optimal robot-environment interaction control, respectively. Finally, adaptive neural assist-as-needed control is developed for assistive robotic rehabilitation.
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Rahimi, H.N.; Howard, Ian; Cui, Lei (2018)The problem of assistive human–robot interaction (HRI) with unknown impedance parameters is nontrivial and interesting. This problem becomes even more challenging if unknown reference trajectory and uncertain robot dynamics ...
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Weber, Keven (1998)Giving robots the ability to move around autonomously in various real-world environments has long been a major challenge for Artificial Intelligence. New approaches to the design and control of autonomous robots have shown ...